import numpy as np

llf = np.array([-245.40783909604])

nobs = np.array([             202])

k = np.array([               5])

k_exog = np.array([               1])

sigma = np.array([  .8100467417583])

chi2 = np.array([   2153.20304012])

df_model = np.array([               3])

k_ar = np.array([               1])

k_ma = np.array([               2])

params = np.array([ .92817025087557, 
                   -.89593490671979, 
                    1.3025011610587, 
                    .30250063082791, 
                     .8100467417583])

cov_params = np.array([ .00638581549851, 
                         .0001858475428, 
                        2.8222806545671, 
                         .8538806860364, 
                       -1.1429127085819, 
                         .0001858475428, 
                        .00132037832566, 
                       -.14420925344502, 
                       -.04447007102804, 
                         .0576156187095, 
                        2.8222806545671, 
                       -.14420925344502, 
                        40397.568324803, 
                        12222.977216556, 
                       -16359.547340433, 
                         .8538806860364, 
                       -.04447007102804, 
                        12222.977216556, 
                        3698.2722243412, 
                       -4949.8609964351, 
                       -1.1429127085819, 
                         .0576156187095, 
                       -16359.547340433, 
                       -4949.8609964351, 
                        6625.0231409853]).reshape(5,5)

xb = np.array([ .92817026376724, 
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                1.1353877782822])

y = np.array([np.nan, 
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resid = np.array([np.nan, 
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yr = np.array([np.nan, 
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                .65953236818314, 
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                .59174418449402, 
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                 .7956041097641, 
               -.47337827086449, 
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                .80359941720963, 
                .41915187239647, 
               -.02237024717033, 
                 1.039052605629, 
                 -.409359395504, 
                3.7650005817413, 
               -2.2902269363403, 
                1.5579376220703, 
                  .072744384408, 
               -1.3312624692917, 
                .90316116809845, 
                1.3147799968719, 
               -.21801064908504, 
                1.1927837133408, 
                1.7281278371811, 
                .15252174437046, 
                3.4807071685791, 
               -3.9110956192017, 
               -3.9886209964752, 
                .86727404594421, 
                .55887448787689, 
                .78061258792877])

mse = np.array([ .77732294797897, 
                 .77732294797897, 
                 .70387578010559, 
                 .69261533021927, 
                 .68906670808792, 
                 .68708789348602, 
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                  .6831266283989, 
                 .68206071853638, 
                 .68107759952545, 
                 .68016695976257, 
                 .67932069301605, 
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                 .67710596323013, 
                 .67645901441574, 
                  .6758508682251, 
                 .67527812719345, 
                 .67473775148392, 
                 .67422717809677, 
                 .67374390363693, 
                 .67328584194183, 
                  .6728510260582, 
                 .67243778705597, 
                 .67204451560974, 
                 .67166984081268, 
                 .67131245136261, 
                 .67097115516663, 
                 .67064493894577, 
                 .67033278942108, 
                 .67003381252289, 
                 .66974723339081, 
                 .66947221755981, 
                 .66920816898346, 
                 .66895437240601, 
                 .66871029138565, 
                 .66847538948059, 
                 .66824907064438, 
                 .66803097724915, 
                 .66782057285309, 
                 .66761755943298, 
                 .66742146015167, 
                 .66723203659058, 
                 .66704881191254, 
                 .66687160730362, 
                 .66670006513596, 
                 .66653394699097, 
                 .66637301445007, 
                 .66621696949005, 
                 .66606563329697, 
                 .66591882705688, 
                 .66577625274658, 
                 .66563785076141, 
                 .66550332307816, 
                 .66537261009216, 
                  .6652455329895, 
                 .66512185335159, 
                 .66500157117844, 
                 .66488444805145, 
                 .66477036476135, 
                 .66465926170349, 
                 .66455101966858, 
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                  .6643425822258, 
                 .66424214839935, 
                 .66414421796799, 
                 .66404861211777, 
                 .66395533084869, 
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                 .66360312700272, 
                 .66351997852325, 
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                  .6633592247963, 
                 .66328144073486, 
                 .66320532560349, 
                 .66313081979752, 
                 .66305786371231, 
                 .66298645734787, 
                  .6629164814949, 
                 .66284799575806, 
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                 .66265046596527, 
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                 .66102415323257, 
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                 .66095358133316, 
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                 .66085147857666, 
                 .66081839799881, 
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                 .66075360774994, 
                 .66072189807892, 
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                 .66065979003906, 
                 .66062939167023, 
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                 .66056972742081, 
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                 .66048324108124, 
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                 .66032004356384, 
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                 .66026836633682, 
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                 .65948438644409, 
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                 .65940302610397, 
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                 .65935611724854, 
                 .65934079885483, 
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                 .65925180912018, 
                 .65923744440079, 
                 .65922319889069, 
                 .65920913219452, 
                 .65919518470764, 
                 .65918135643005])

stdp = np.array([ .92817026376724, 
                  .92817026376724, 
                  .69511789083481, 
                  .77192437648773, 
                  .66135895252228, 
                  .77525061368942, 
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                  .69971066713333, 
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                  .67785596847534, 
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                   .7017787694931, 
                  .75292426347733, 
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                  .78219056129456, 
                  .78040039539337, 
                  .71250075101852, 
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                  .78457218408585, 
                  .83697980642319, 
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                  .78025943040848, 
                  .72790426015854, 
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                  .78669738769531, 
                  .73868823051453, 
                  .78071022033691, 
                  .78002023696899, 
                  .83737623691559, 
                  .98988044261932, 
                  .72882527112961, 
                  1.2245427370071, 
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                  .85591268539429, 
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                  .97817331552505, 
                  1.4012540578842, 
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                   .7919961810112, 
                  1.2807723283768, 
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                  .68923074007034, 
                  1.6174983978271, 
                  .64740318059921, 
                  1.4949930906296, 
                  1.2678960561752, 
                  1.0586776733398, 
                  .55762887001038, 
                  1.2790743112564, 
                  .66515874862671, 
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                  .70554333925247, 
                  1.2391568422318, 
                  .75241559743881, 
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                  .69235223531723, 
                  1.0785228013992, 
                   .8043577671051, 
                  1.0037930011749, 
                  .78750842809677, 
                  1.1880930662155, 
                  .74399447441101, 
                  1.1791603565216, 
                  .85870295763016, 
                  1.0032330751419, 
                   .8019300699234, 
                  1.1696527004242, 
                  .92376220226288, 
                  .99186056852341, 
                  .94733852148056, 
                  1.0748032331467, 
                  .64247089624405, 
                  .95419937372208, 
                  .92043441534042, 
                   .8104555606842, 
                  .66252142190933, 
                  1.1178470849991, 
                  .69223344326019, 
                  1.0570795536041, 
                  .90239083766937, 
                  .95320242643356, 
                  1.0541093349457, 
                  1.0082466602325, 
                  1.1376332044601, 
                  1.1841852664948, 
                  .90440809726715, 
                  1.2733660936356, 
                  .66835701465607, 
                  1.1515763998032, 
                  .44600257277489, 
                  .93500959873199, 
                  1.0847823619843, 
                  .83353632688522, 
                  1.0442448854446, 
                   1.077241897583, 
                  .71010553836823, 
                  .89557945728302, 
                  1.0163468122482, 
                   1.094814658165, 
                  .89641278982162, 
                  1.2808450460434, 
                  1.0223702192307, 
                  .96094745397568, 
                   1.309353351593, 
                  .73499941825867, 
                  2.4902238845825, 
                  -.2579345703125, 
                  1.9272556304932, 
                  .53125941753387, 
                   .7708500623703, 
                  1.0312130451202, 
                  1.6360099315643, 
                   .6022145152092, 
                  1.6338716745377, 
                  1.3494771718979, 
                  1.1322995424271, 
                  2.1901025772095, 
                 -.72639065980911, 
                 -.37026473879814, 
                  1.2391144037247, 
                  1.1353877782822])

icstats = np.array([             202, 
                    np.nan, 
                    -245.40783909604, 
                                   5, 
                     500.81567819208, 
                     517.35701667909])


class Bunch(dict):
    def __init__(self, **kw):
        dict.__init__(self, kw)
        self.__dict__  = self


results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, )
